--- title: Title keywords: fastai sidebar: home_sidebar summary: "summary" description: "summary" ---
some_function_to_run_on_cloud
[source]
some_function_to_run_on_cloud
()
!python3 -m gcp_runner.entry_point --module-name='gcp_runner.sample_code' --function-name='some_function_to_run_on_cloud'
!docker run -v `pwd`/gcp_runner:/gcp_runner 'gcr.io/deeplearning-platform-release/tf2-cpu.2-1' python3 -u -m gcp_runner.entry_point --module-name='gcp_runner.sample_code' --function-name='some_function_to_run_on_cloud'
!gcloud ai-platform local train --job-dir='job-dir' --module-name=gcp_runner.entry_point \
--package-path=`pwd`/gcp_runner -- \
--module-name='gcp_runner.sample_code' --function-name='some_function_to_run_on_cloud'
!ls
!docker build -f Dockerfile -t gcr.io/alekseyv-scalableai-dev/tf2-cpu.2-1 ./
from nbdev.export import *
notebook2script()
Converted 00_core.ipynb. Converted 01_ai_platform_constants.ipynb. Converted ai_platform_runner.ipynb. Converted entry_point.ipynb. Converted index.ipynb. Converted kubernetes_runner.ipynb. Converted local_runner.ipynb. Converted sample_code_test.ipynb.
from gcp_runner.core import export_and_reload_all
export_and_reload_all(silent=True, ignore_errors=False)
import gcp_runner.ai_platform_runner
gcp_runner.ai_platform_runner.run_package(
some_function_to_run_on_cloud,
'gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir')
import gcp_runner.kubernetes_runner
from gcp_runner import ai_platform_constants
gcp_runner.kubernetes_runner.run_docker_image(
some_function_to_run_on_cloud,
'gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir',
image_uri='gcr.io/alekseyv-scalableai-dev/tf2-cpu.2-1',
distribution_strategy_type=ai_platform_constants.DistributionStrategyType.MIRRORED_STRATEGY,
use_distribution_strategy_scope=True,
dry_run=True)
To see job output in tensorboard, run following command: tensorboard --logdir=%s gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir Generating kubernetes YAML file: kind: Service apiVersion: v1 metadata: name: kubernetes-runner-train-docker-chief-0 spec: type: ClusterIP clusterIP: None selector: job: kubernetes-runner-train-docker-chief-0 ports: - port: 5000 --- kind: Pod apiVersion: v1 metadata: name: kubernetes-runner-train-docker-chief-0 labels: job: kubernetes-runner-train-docker-chief-0 annotations: tf-version.cloud-tpus.google.com: "2.1" spec: hostNetwork: true dnsPolicy: ClusterFirstWithHostNet restartPolicy: Never containers: - name: gcp-runner-image image: gcr.io/alekseyv-scalableai-dev/tf2-cpu.2-1 imagePullPolicy: Always resources: limits: cpu: 3 memory: 12G nvidia.com/gpu: 2 env: - name: TF_CONFIG value: "{ \"cluster\": {\"chief\": [\"kubernetes-runner-train-docker-chief-0:5000\"]}, \"task\": { \"type\": \"chief\", \"index\": 0 }, \"environment\": \"cloud\" }" ports: - containerPort: 5000 command: - "python" - "-u" - "-m" - "gcp_runner.entry_point" - "--module-name=gcp_runner.sample_code_test" - "--function-name=some_function_to_run_on_cloud" - "--job-dir=gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir" - "--distribution-strategy-type=tf.distribute.MirroredStrategy" - "--use-distribution-strategy-scope" --- Deleting all resources on Kubernetes cluster: kubectl delete namespace kubernetes-runner-namespace namespace "kubernetes-runner-namespace" deleted Creating Kubernetes kubernetes-runner-namespace namespace: kubectl create namespace kubernetes-runner-namespace namespace/kubernetes-runner-namespace created Creating Kubernetes cluster: kubectl create -f /var/folders/0c/_9t2rb612zq01d325yh__5ph0069sb/T/tmprq5bw0ib --namespace=kubernetes-runner-namespace service/kubernetes-runner-train-docker-chief-0 created pod/kubernetes-runner-train-docker-chief-0 created See logs at: https://pantheon.corp.google.com/logs/viewer?minLogLevel=0&expandAll=false&advancedFilter=resource.type%3D%22container%22%0Aresource.labels.namespace_id%3D%22kubernetes-runner-namespace%22&dateRangeStart=2020-04-01T23:32:11Z Polling Kubernetes logs: logs since 2020-04-01T23:32:11+00:00 kubernetes-runner-train-docker-chief-0 in gcp_runner entry point kubernetes-runner-train-docker-chief-0 running entrypoint function: gcp_runner.sample_code_test.some_function_to_run_on_cloud kubernetes-runner-train-docker-chief-0 additional args: ['--job-dir=gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir', '--distribution-strategy-type=tf.distribute.MirroredStrategy', '--use-distribution-strategy-scope'] kubernetes-runner-train-docker-chief-0 /root/gcp_runner/entry_point.py:30: DeprecationWarning: inspect.getargspec() is deprecated since Python 3.0, use inspect.signature() or inspect.getfullargspec() kubernetes-runner-train-docker-chief-0 args_spec = inspect.getargspec(func) kubernetes-runner-train-docker-chief-0 running sample_code_test.some_function_to_run_on_cloud kubernetes-runner-train-docker-chief-0 in main before sleep 1 kubernetes-runner-train-docker-chief-0 in main after sleep 2 kubernetes-runner-train-docker-chief-0 in main after sleep 3 kubernetes-runner-train-docker-chief-0 in main after sleep 4 logs since 2020-04-01T23:33:12+00:00 No resources found. logs since 2020-04-01T23:33:12+00:00 No resources found. logs since 2020-04-01T23:33:12+00:00 No resources found. logs since 2020-04-01T23:33:12+00:00 No resources found. logs since 2020-04-01T23:33:12+00:00 kubernetes-runner-train-docker-chief-0 in gcp_runner entry point kubernetes-runner-train-docker-chief-0 running entrypoint function: criteo_nbdev.trainer.train_and_evaluate_keras_model_small kubernetes-runner-train-docker-chief-0 2020-04-01 23:36:43.936664: I tensorflow_io/core/kernels/cpu_check.cc:127] Your CPU supports instructions that this TensorFlow IO binary was not compiled to use: AVX2 FMA kubernetes-runner-train-docker-chief-0 additional args: ['--job-dir=gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir/model_mirrored_strategy_3'] kubernetes-runner-train-docker-chief-0 /root/gcp_runner/gcp_runner/entry_point.py:79: DeprecationWarning: inspect.getargspec() is deprecated since Python 3.0, use inspect.signature() or inspect.getfullargspec() kubernetes-runner-train-docker-chief-0 args_spec = inspect.getargspec(func) kubernetes-runner-train-docker-chief-0 ArgSpec(args=['distribution_strategy', 'job_dir'], varargs=None, keywords='kwargs', defaults=(None, None)) kubernetes-runner-train-docker-chief-0 Traceback (most recent call last): File "/opt/conda/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) File "/opt/conda/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/root/gcp_runner/gcp_runner/entry_point.py", line 101, in <module> main() File "/root/gcp_runner/gcp_runner/entry_point.py", line 88, in main distribution_strategy_type = DistributionStrategyType(args.distribution_strategy) AttributeError: 'Namespace' object has no attribute 'distribution_strategy' kubernetes-runner-train-docker-chief-0 Error detected in gke_instances logs since 2020-04-01T23:38:19+00:00 No resources found. logs since 2020-04-01T23:38:19+00:00 No resources found. logs since 2020-04-01T23:38:19+00:00 No resources found. logs since 2020-04-01T23:38:19+00:00 No resources found. logs since 2020-04-01T23:38:19+00:00 No resources found. logs since 2020-04-01T23:38:19+00:00 No resources found. logs since 2020-04-01T23:38:19+00:00 No resources found. logs since 2020-04-01T23:38:19+00:00 No resources found. logs since 2020-04-01T23:38:19+00:00 kubernetes-runner-train-docker-chief-0 in gcp_runner entry point kubernetes-runner-train-docker-chief-0 running entrypoint function: criteo_nbdev.trainer.train_and_evaluate_keras_model_small kubernetes-runner-train-docker-chief-0 2020-04-01 23:45:43.027940: I tensorflow_io/core/kernels/cpu_check.cc:127] Your CPU supports instructions that this TensorFlow IO binary was not compiled to use: AVX2 FMA kubernetes-runner-train-docker-chief-0 additional args: ['--job-dir=gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir/model_mirrored_strategy_3'] kubernetes-runner-train-docker-chief-0 /root/gcp_runner/gcp_runner/entry_point.py:79: DeprecationWarning: inspect.getargspec() is deprecated since Python 3.0, use inspect.signature() or inspect.getfullargspec() kubernetes-runner-train-docker-chief-0 args_spec = inspect.getargspec(func) kubernetes-runner-train-docker-chief-0 ArgSpec(args=['distribution_strategy', 'job_dir'], varargs=None, keywords='kwargs', defaults=(None, None)) kubernetes-runner-train-docker-chief-0 Traceback (most recent call last): File "/opt/conda/lib/python3.7/runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) File "/opt/conda/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/root/gcp_runner/gcp_runner/entry_point.py", line 101, in <module> main() File "/root/gcp_runner/gcp_runner/entry_point.py", line 88, in main distribution_strategy_type = DistributionStrategyType(args.distribution_strategy) AttributeError: 'Namespace' object has no attribute 'distribution_strategy' logs since 2020-04-01T23:47:34+00:00 No resources found. logs since 2020-04-01T23:47:34+00:00 No resources found. logs since 2020-04-01T23:47:34+00:00 No resources found. logs since 2020-04-01T23:47:34+00:00 No resources found. logs since 2020-04-01T23:47:34+00:00 kubernetes-runner-train-docker-chief-0 in gcp_runner entry point kubernetes-runner-train-docker-chief-0 running entrypoint function: criteo_nbdev.trainer.train_and_evaluate_keras_model_small kubernetes-runner-train-docker-chief-0 2020-04-01 23:52:01.264348: I tensorflow_io/core/kernels/cpu_check.cc:127] Your CPU supports instructions that this TensorFlow IO binary was not compiled to use: AVX2 FMA kubernetes-runner-train-docker-chief-0 additional args: ['--job-dir=gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir/model_mirrored_strategy_3'] kubernetes-runner-train-docker-chief-0 ArgSpec(args=['distribution_strategy', 'job_dir'], varargs=None, keywords='kwargs', defaults=(None, None)) kubernetes-runner-train-docker-chief-0 /root/gcp_runner/gcp_runner/entry_point.py:79: DeprecationWarning: inspect.getargspec() is deprecated since Python 3.0, use inspect.signature() or inspect.getfullargspec() kubernetes-runner-train-docker-chief-0 args_spec = inspect.getargspec(func) kubernetes-runner-train-docker-chief-0 2020-04-01 23:52:01.752317: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz kubernetes-runner-train-docker-chief-0 2020-04-01 23:52:01.752914: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5628ab0053b0 initialized for platform Host (this does not guarantee that XLA will be used). Devices: kubernetes-runner-train-docker-chief-0 2020-04-01 23:52:01.752956: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce. kubernetes-runner-train-docker-chief-0 running code in %s scope tf.distribute.MirroredStrategy kubernetes-runner-train-docker-chief-0 distribution_strategy: kubernetes-runner-train-docker-chief-0 <tensorflow.python.distribute.mirrored_strategy.MirroredStrategy object at 0x7fc936ce9890> kubernetes-runner-train-docker-chief-0 job_dir: kubernetes-runner-train-docker-chief-0 gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir/model_mirrored_strategy_3 kubernetes-runner-train-docker-chief-0 kwargs: kubernetes-runner-train-docker-chief-0 {'distribution_strategy_type': <DistributionStrategyType.MIRRORED_STRATEGY: 'tf.distribute.MirroredStrategy'>} kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4267: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. kubernetes-runner-train-docker-chief-0 Instructions for updating: kubernetes-runner-train-docker-chief-0 The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4267: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. kubernetes-runner-train-docker-chief-0 Instructions for updating: kubernetes-runner-train-docker-chief-0 The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4322: HashedCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. kubernetes-runner-train-docker-chief-0 Instructions for updating: kubernetes-runner-train-docker-chief-0 The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4322: HashedCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. kubernetes-runner-train-docker-chief-0 Instructions for updating: kubernetes-runner-train-docker-chief-0 The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead. kubernetes-runner-train-docker-chief-0 Train for 8789 steps, validate for 976 steps kubernetes-runner-train-docker-chief-0 Epoch 1/2 kubernetes-runner-train-docker-chief-0 2020-04-01 23:52:25.479759: I tensorflow/core/profiler/lib/profiler_session.cc:225] Profiler session started. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.647987). Check your callbacks. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.647987). Check your callbacks. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 No resources found. logs since 2020-04-01T23:52:42+00:00 kubernetes-runner-train-docker-chief-0 kubernetes-runner-train-docker-chief-0 Epoch 00001: saving model to gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir/model_mirrored_strategy_3/checkpoints/epochs:001-accuracy:0.757.hdf5 kubernetes-runner-train-docker-chief-0 8789/8789 - 801s - loss: 0.5118 - accuracy: 0.7573 - val_loss: 0.5130 - val_accuracy: 0.7641 kubernetes-runner-train-docker-chief-0 Epoch 2/2 logs since 2020-04-02T00:06:04+00:00 No resources found. logs since 2020-04-02T00:06:04+00:00 No resources found. logs since 2020-04-02T00:06:04+00:00 No resources found. logs since 2020-04-02T00:06:04+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 17s Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:06:04+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 80s Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:06:04+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 2m22s Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:06:04+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 3m25s Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:06:04+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 4m27s Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:06:04+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 5m28s Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:06:04+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 6m30s Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:06:04+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 7m31s Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:06:04+00:00 kubernetes-runner-train-docker-chief-0 2020-04-02 00:17:37.582232: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled logs since 2020-04-02T00:18:25+00:00 kubernetes-runner-train-docker-chief-0 kubernetes-runner-train-docker-chief-0 Epoch 00002: saving model to gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir/model_mirrored_strategy_3/checkpoints/epochs:002-accuracy:0.763.hdf5 kubernetes-runner-train-docker-chief-0 8789/8789 - 781s - loss: 0.5017 - accuracy: 0.7632 - val_loss: 0.5045 - val_accuracy: 0.7677 kubernetes-runner-train-docker-chief-0 2020-04-02 00:18:38.064137: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled kubernetes-runner-train-docker-chief-0 2020-04-02 00:18:38.385227: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled logs since 2020-04-02T00:19:27+00:00 kubernetes-runner-train-docker-chief-0 976/976 - 57s - loss: 0.5042 - accuracy: 0.7679 kubernetes-runner-train-docker-chief-0 2020-04-02 00:19:39.702756: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled kubernetes-runner-train-docker-chief-0 Model: "sequential" kubernetes-runner-train-docker-chief-0 _________________________________________________________________ kubernetes-runner-train-docker-chief-0 Layer (type) Output Shape Param # kubernetes-runner-train-docker-chief-0 ================================================================= kubernetes-runner-train-docker-chief-0 feature_layer (DenseFeatures multiple 669098 kubernetes-runner-train-docker-chief-0 _________________________________________________________________ kubernetes-runner-train-docker-chief-0 dropout (Dropout) multiple 0 kubernetes-runner-train-docker-chief-0 _________________________________________________________________ kubernetes-runner-train-docker-chief-0 dense (Dense) multiple 541190 kubernetes-runner-train-docker-chief-0 _________________________________________________________________ kubernetes-runner-train-docker-chief-0 dense_1 (Dense) multiple 358202 kubernetes-runner-train-docker-chief-0 _________________________________________________________________ kubernetes-runner-train-docker-chief-0 dense_2 (Dense) multiple 358202 kubernetes-runner-train-docker-chief-0 _________________________________________________________________ kubernetes-runner-train-docker-chief-0 dense_3 (Dense) multiple 599 kubernetes-runner-train-docker-chief-0 ================================================================= kubernetes-runner-train-docker-chief-0 Total params: 1,927,291 kubernetes-runner-train-docker-chief-0 Trainable params: 1,927,291 kubernetes-runner-train-docker-chief-0 Non-trainable params: 0 kubernetes-runner-train-docker-chief-0 _________________________________________________________________ logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 11m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 12m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 13m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 14m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 15m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 16m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 17m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 18m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 19m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 20m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 21m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 22m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 23m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 24m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 25m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 27m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 28m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 29m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 30m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 31m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 32m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 33m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 34m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 35m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 36m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 37m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 38m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 39m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 40m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 41m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 42m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 43m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 44m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 45m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 46m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 47m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 48m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 49m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 50m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 51m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 52m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 53m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 54m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 55m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 56m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 57m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 58m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 59m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 LAST SEEN TYPE REASON OBJECT MESSAGE 60m Warning NodeSysctlChange node/gke-criteo-cluster-tpu-default-pool-e0bb0d92-dtjp {"unmanaged": {"kernel.tainted": "12289"}} logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 No resources found. logs since 2020-04-02T00:20:28+00:00 kubernetes-runner-train-docker-chief-0 in gcp_runner entry point kubernetes-runner-train-docker-chief-0 running entrypoint function: criteo_nbdev.trainer.train_and_evaluate_keras_model_small kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:03.601224: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:05.229800: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer.so.6 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:05.761741: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer_plugin.so.6 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:06.546114: I tensorflow_io/core/kernels/cpu_check.cc:127] Your CPU supports instructions that this TensorFlow IO binary was not compiled to use: AVX2 FMA kubernetes-runner-train-docker-chief-0 additional args: ['--job-dir=gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir/model_mirrored_strategy_alekseyv_20200401_184228'] kubernetes-runner-train-docker-chief-0 /root/gcp_runner/gcp_runner/entry_point.py:79: DeprecationWarning: inspect.getargspec() is deprecated since Python 3.0, use inspect.signature() or inspect.getfullargspec() kubernetes-runner-train-docker-chief-0 args_spec = inspect.getargspec(func) kubernetes-runner-train-docker-chief-0 ArgSpec(args=['distribution_strategy', 'job_dir'], varargs=None, keywords='kwargs', defaults=(None, None)) kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:07.264067: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:07.531275: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:07.532374: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties: kubernetes-runner-train-docker-chief-0 pciBusID: 0000:00:04.0 name: Tesla K80 computeCapability: 3.7 kubernetes-runner-train-docker-chief-0 coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:07.532653: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:07.533644: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 1 with properties: kubernetes-runner-train-docker-chief-0 pciBusID: 0000:00:05.0 name: Tesla K80 computeCapability: 3.7 kubernetes-runner-train-docker-chief-0 coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:07.533741: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:07.533849: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:07.735606: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:07.874661: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.494455: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.634299: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.634592: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.634878: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.636008: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.637094: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.638245: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.639194: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0, 1 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.647752: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.648218: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x557508c47e50 initialized for platform Host (this does not guarantee that XLA will be used). Devices: kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.648264: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.914778: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.916836: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.918113: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x557509448d30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.918151: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla K80, Compute Capability 3.7 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.918162: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (1): Tesla K80, Compute Capability 3.7 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.919029: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.919966: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties: kubernetes-runner-train-docker-chief-0 pciBusID: 0000:00:04.0 name: Tesla K80 computeCapability: 3.7 kubernetes-runner-train-docker-chief-0 coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.920170: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.921097: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 1 with properties: kubernetes-runner-train-docker-chief-0 pciBusID: 0000:00:05.0 name: Tesla K80 computeCapability: 3.7 kubernetes-runner-train-docker-chief-0 coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.921204: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.921247: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.921326: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.921382: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.921464: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.921518: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.921563: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.921740: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.922788: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.923824: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.924830: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.925708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0, 1 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:08.925818: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:10.445594: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix: kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:10.445664: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] 0 1 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:10.445681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0: N N kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:10.445690: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 1: N N kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:10.446154: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:10.447225: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:10.448256: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:10.449199: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10714 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7) kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:10.450025: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:10.450975: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10714 MB memory) -> physical GPU (device: 1, name: Tesla K80, pci bus id: 0000:00:05.0, compute capability: 3.7) kubernetes-runner-train-docker-chief-0 running code in %s scope tf.distribute.MirroredStrategy kubernetes-runner-train-docker-chief-0 distribution_strategy: kubernetes-runner-train-docker-chief-0 <tensorflow.python.distribute.mirrored_strategy.MirroredStrategy object at 0x7f18a7330090> kubernetes-runner-train-docker-chief-0 job_dir: kubernetes-runner-train-docker-chief-0 gs://alekseyv-scalableai-dev-criteo-model-bucket/test-job-dir/model_mirrored_strategy_alekseyv_20200401_184228 kubernetes-runner-train-docker-chief-0 kwargs: kubernetes-runner-train-docker-chief-0 {'distribution_strategy_type': <DistributionStrategyType.MIRRORED_STRATEGY: 'tf.distribute.MirroredStrategy'>} kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4267: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. kubernetes-runner-train-docker-chief-0 Instructions for updating: kubernetes-runner-train-docker-chief-0 The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4267: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. kubernetes-runner-train-docker-chief-0 Instructions for updating: kubernetes-runner-train-docker-chief-0 The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4322: HashedCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. kubernetes-runner-train-docker-chief-0 Instructions for updating: kubernetes-runner-train-docker-chief-0 The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4322: HashedCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. kubernetes-runner-train-docker-chief-0 Instructions for updating: kubernetes-runner-train-docker-chief-0 The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead. kubernetes-runner-train-docker-chief-0 Train for 8789 steps, validate for 976 steps kubernetes-runner-train-docker-chief-0 Epoch 1/2 kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:Efficient allreduce is not supported for 24 IndexedSlices kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:Efficient allreduce is not supported for 24 IndexedSlices logs since 2020-04-02T01:46:46+00:00 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:50.258722: I tensorflow/core/profiler/lib/profiler_session.cc:225] Profiler session started. kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:50.259180: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1259] Profiler found 2 GPUs kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:50.259874: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcupti.so.10.1'; dlerror: libcupti.so.10.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:50.260194: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1307] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error CUPTI could not be loaded or symbol could not be found. kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:50.260436: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1346] function cupti_interface_->ActivityRegisterCallbacks( AllocCuptiActivityBuffer, FreeCuptiActivityBuffer)failed with error CUPTI could not be loaded or symbol could not be found. kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:50.380375: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1329] function cupti_interface_->EnableCallback( 0 , subscriber_, CUPTI_CB_DOMAIN_DRIVER_API, cbid)failed with error CUPTI could not be loaded or symbol could not be found. kubernetes-runner-train-docker-chief-0 2020-04-02 01:46:50.380486: I tensorflow/core/profiler/internal/gpu/device_tracer.cc:88] GpuTracer has collected 0 callback api events and 0 activity events. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (1.205124). Check your callbacks. kubernetes-runner-train-docker-chief-0 WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (1.205124).


Woman crush Wednesday: From being a beach bum to a fitness freak Shibani Dandekar's pics proves that she is a 'modern-day diva'
by TIMESOFINDIA.COMShibani Dandekar is more than just a pretty face! From being a fitness freak, an animal lover to doling out major fashion inspiration, Shibani is a 'modern-day diva' in a true sense. The diva is well-known for her ultra-glam avatar, be it on the small screen or cameo appearances on the silver screen.
Shibani is exactly the one we would love to crush on Wednesday. When we scrolled down her Instagram, we were in awe of her stunning and exhilarating posts.
The model turned actress never fails to make heads turn with her sartorial choices. These clicks will certainly leave you lusting after her enviable wardrobe.
 She often shares her sizzling bikini-clad pictures posing by the beach side.
Like boyfriend Farhan Akhtar, Shibani is also a fitness freak. She follows her diet and daily routine strictly. Her workout pictures and videos are truly inspiring.
Although being fit is on the top of her list, the actress is also a full-blown foodie. Her love and affection for food can be seen through these clicks.
Shibani is often seen showing unconditional love to her furry friends. Her Instagram profile includes many adorable pictures of the actress posing with her pet. Also, she has shared a few pics along with different animals from her travel diaries.


MIT Manipal, 2016-2020
B.Tech, Computers and Communication
8.52/10
Delhi Public School, Navi Mumbai, 2016
CBSE
90.6/100
Kendriya Vidyalaya, Mankhurd, 2014
CBSE
10/10
Hey, I'm Arvind.
I'm a full stack developer, kinda.
I'm also a competitive coder, and I love to learn.
B.Tech, Computers and Communication
8.52/10
CBSE
90.6/100
CBSE
10/10
Built a project using Java Servlets and JSP that could import and export .xlsx files from a mySQL database. Various CSS and JS frameworks were used to provide a better end user experience.
Build front end for all portals used during the fest. The system admin core committee, along with 25 organizers, automate the fest and build end to end solutions for various categories.
Proficient with HTML5, CSS3, vanilla JS and jQuery.
Working knowledge of VueJS, ReactJS and Redux.
Primarily use NodeJS with Express and mySQL.
Working knowledge of Java Servlets and PHP.
Good with DevTools and Postman.
Proficient with basic data structures and algorithms.
Primarily use C++, though I often use C, Python and Ruby.
Readable, modular self documenting code.
Amateur Photographer
I might start a blog soon?


- s_key - Variable in class ParemeterBeans.CredentialBean
-
- ScanAllInstances(UUIDBean) - Method in class ServerAPI
-
This is the method which calls for a scan of all instances
- scanContext() - Method in class Scanner.InstanceScannerThread
-
- scanContext() - Method in class Scanner.SubNetworkScannerThread
-
- scanContext() - Method in interface Scanner.ThreadedScannerInterface
-
Launches a sub-scanner to only scan at specific point
void scanOnly();
/**
Launches a default sub-scanner that sub-scans entire region.
- scanContext() - Method in class Scanner.VpcScannerThread
-
- ScanFrom(UUIDBean) - Method in class ServerAPI
-
This is the method which calls for a scan beginning at specified uuid
- scanFullNetwork() - Method in class Scanner.AWSScanner
-
- scanFullNetwork() - Method in interface Scanner.ScannerInterface
-
Launches a full scan of the network
- Scanner - package Scanner
-
- ScannerInterface - Interface in Scanner
-
- ScanNetwork() - Method in class ServerAPI
-
This is the method which calls for a full scan of the network
- scanNetworkFrom(String, String) - Method in class Scanner.AWSScanner
-
- scanNetworkFrom(String, String) - Method in interface Scanner.ScannerInterface
-
Launches a scan of the nentwork from specific point
- scanOnly() - Method in class Scanner.InstanceScannerThread
-
- scanOnly() - Method in class Scanner.SubNetworkScannerThread
-
- scanOnly() - Method in class Scanner.VpcScannerThread
-
- scanRegion(String) - Method in class Scanner.AWSScanner
-
- scanRegion(String) - Method in interface Scanner.ScannerInterface
-
Launches a regional scan of the network
- ScanRegion(UUIDBean) - Method in class ServerAPI
-
This is the method which calls for a region scan
- ScanUp() - Method in class ServerAPI
-
This is the method which calls for scan to continue from where scanFrom ended off.
- SecurityGroups - package SecurityGroups
-
- securityGroupToHtml(String) - Method in class InformationDecorator.HTMLEncoder
-
- SecurityRule - Class in SecurityGroups
-
Created by Jedd Shneier
- SecurityRule() - Constructor for class SecurityGroups.SecurityRule
-
- SecurityRuleSet - Class in SecurityGroups
-
Created by Jedd Shneier.
- SecurityRuleSet() - Constructor for class SecurityGroups.SecurityRuleSet
-
- ServerAPI - Class in <Unnamed>
-
The Java class will be hosted at the URI path "/services"
- ServerAPI() - Constructor for class ServerAPI
-
- setAccess_key(String) - Method in class Credentials.Credential
-
- setAssociations(LinkedList<String>) - Method in class RouteTableGroups.RouteTableSet
-
- setId(String) - Method in class RouteTableGroups.RouteTableSet
-
- setID(String) - Method in class RouteTableGroups.RouteTableSet
-
- setId(String) - Method in class SecurityGroups.SecurityRuleSet
-
- setIdentifier(String) - Method in class ParemeterBeans.OptionBean
-
- setInboundRules(LinkedList<SecurityRule>) - Method in class SecurityGroups.SecurityRuleSet
-
- setInformation(String) - Method in interface Composite.NetworkTree
-
- setInformation(String) - Method in class Composite.Node
-
- setIpAdresses(List<String>) - Method in class SecurityGroups.SecurityRule
-
- setLevel(int) - Method in interface Composite.NetworkTree
-
- setLevel(int) - Method in class Composite.Node
-
- setLevel(String) - Method in class ParemeterBeans.OptionBean
-
- setName(String) - Method in interface Composite.NetworkTree
-
- setName(String) - Method in class Composite.Node
-
- setOutboundRules(LinkedList<SecurityRule>) - Method in class SecurityGroups.SecurityRuleSet
-
- setParentIdentifier(String) - Method in class Buffer.SharedBuffer
-
- setParentIdentifier(String) - Method in interface Buffer.SmartBufferInterface
-
Sets the parent uuid for scan Up
- setParentLevel(String) - Method in class Buffer.SharedBuffer
-
- setParentLevel(String) - Method in interface Buffer.SmartBufferInterface
-
Sets the parent level for scan Up
- setPortRanges(Integer, Integer) - Method in class SecurityGroups.SecurityRule
-
- setPrivate_key(String) - Method in class Credentials.Credential
-
- setPrivateIpAddress(String) - Method in class SecurityGroups.SecurityRuleSet
-
- setProtocol(String) - Method in class SecurityGroups.SecurityRule
-
- setPublicIpAddress(String) - Method in class SecurityGroups.SecurityRuleSet
-
- setRoutes(LinkedList<String>) - Method in class RouteTableGroups.RouteTableSet
-
- setScannChoice(int) - Method in class ParemeterBeans.OptionBean
-
- setSecurtyGroupIds(LinkedList<String>) - Method in class SecurityGroups.SecurityRule
-
- setUUID(String) - Method in interface Composite.NetworkTree
-
- setUUID(String) - Method in class Composite.Node
-
- setVpcID(String) - Method in class SecurityGroups.SecurityRuleSet
-
- SharedBuffer - Class in Buffer
-
SharedBuffer
- SharedBuffer() - Constructor for class Buffer.SharedBuffer
-
- SmartBufferInterface - Interface in Buffer
-
SmartBufferInterface
- StopScan(UUIDBean) - Method in class ServerAPI
-
This is the method which calls for all active scans to stop
- stopThreads() - Method in class Buffer.SharedBuffer
-
- stopThreads() - Method in interface Buffer.SmartBufferInterface
-
Sets state to stop to notify threads
- SubNetworkScannerThread - Class in Scanner
-
VpcScannerThread
- SubNetworkScannerThread(String, String, String, AmazonEC2, SharedBuffer) - Constructor for class Scanner.SubNetworkScannerThread
-


| realized_power | funding | power_pct | funding_pct | funding_gap | |
|---|---|---|---|---|---|
| branch | |||||
| ussenate | 17.1 | 1,315,307,663.82 | 48.0 | 22.1 | 25.9 |
| ushouse | 11.5 | 1,836,096,480.82 | 32.2 | 30.9 | 1.3 |
| governor | 5.3 | 1,733,553,291.68 | 14.8 | 29.2 | -14.4 |
| statesenate | 1.0 | 425,843,538.99 | 2.9 | 7.2 | -4.3 |
| statehouse | 0.7 | 631,285,374.53 | 2.0 | 10.6 | -8.6 |


Select controller hardware
VID:
PID:
Data DUMP (up to 16 bytes)
Please open %APPDATA%\PoeController\user-settings.json and edit the values of vid and pid to match the ones for your device, close the application and try opening it again


ABOUT ME
- Name - Chris Hawkins
- Age - 23
- Location - Pittsburgh, PA
- High School - Pittsburgh Brashear
- College - Point Park University
- Major - Information Technology
- Graduation - Spring 2018
Welcome To My Website!
I started off at Point Park as a Multimedia major, before switching to Information Technology during my third year. While working through this major, I decided I want to become a front end web developer or web designer, as I like building the display, or front end, of websites. Right now, I mainly work with HTML5 and CSS3, but am also continuing to learn and work with JavaScript as well.
I am a huge sports fan, and my favorite teams are the Pittsburgh Pirates and Steelers. I have played baseball my whole life and I still play it during the summer.
I currently have a part time job working for Landmark Event Staffing Services. I started working here as a fundraiser with the Point Park University baseball team before becoming employed. I mainly work the Pittsburgh Steelers and Pitt Panther football games at Heinz Field.


@()(implicit req: Request[AnyContent], bot: lib.Bot) @main {
An aid for tracking deployment, especially continuous-deployment, prout is a bot that tells you when your GitHub pull requests are live.
Follow the 4-step program:
The GitHub readme has more details if you need them!


Autodesk Asia Pte Ltd - Data Platform Team in ASRD (Dec 2014 – May 2018) as Senior DevOps Engineer
Responsibilities:
- Autodesk Data Platform DevOPS tasks and projects
- Support multiple legacy projects of ADP
- Other operation and DevOPS related tasks
Achievements:
- Took over multiple legacy projects support in short time. Improve and automate daily operations and act as ONEMAN operation for those products for more than two years
- Built up infrastructure of Autodesk Data Platform based on AWS / Mesos / Marathon / Docker / Ochopod etc. technology stack, including deployment, monitoring, operation, centralized log and so forth
- Setup and popularize the Infrastructure-as-Code solution in Autodesk Data Platform team
- Setup CI / CD workflows of Autodesk Data Platform based Mesos / Marathon / Docker based platform over AWS
- Help to grow up the DevOPS team(From one man to four)
- Planed and started to migrate Autodesk Data Platform to complete AWS stack, e.g. AWS ECS Fargate, Lambda as serverless solution, CI/CD based AWS CodeBuild, CodePipeline and so forth
- Other improvements to help team and members
Autodesk Software(China) R&D Ltd Co. - Consumer Group (Dec 2012 – Nov 2014) as Senior Web Infrastructure Engineer
Responsibilities:
- AWS Cloud Infrastructure management for multiple projects
- Web/Online application architect advise and construct
- Integrate applications to Autodesk Cloud Platform(ECS) with AWS
- CI/CD over Cloud Infrastructure
Achievements:
- Support multiple AWS accounts for more than ten different Autodesk ACG applications / sites HomeSyler.com | Sketchbook.cn | 123Dapp.com | Seek.autodesk.com | spark.autodesk.com | TinkerCAD.com …
- Migrate TinkerCAD.com from Linode to AWS
- Improvement of CI/CD over Cloud infrastructure
- Roll out Web Infrastructure Operation team On-Call rotation following sunset by a four sites based team in global
- Operation tools based on AWS API and Ansible
- Trainer of Cloud Operation Course of ACRD
eBay China Operation Center (Dec 2008 – Dec 2012) as Senior Unix Administrator / Staff Tools Engineer(from Nov 2011)
Responsibilities:
- GMS System Administrator
- Operation Tools developing based on Python / Django/ vSphere SDK, include auto provisioning as IaaS
- ODB(CMDB of eBay operation) developing and maintaining for operation automation
- Cloud Platform(eBay private cloud based on vSphere and Openstack …) usage and integration
- Technique Support and Daily Operation for the Tools Chain(monitor, automation and etc tools) used by global eBay Operation Team(Site NOC)
- Major Tools used by eBay Operation Team(NOC) we supported, Tivoli(EP,ITM and TEC), NerveCenter, NetView, Nagios, Cricket, ActiveMQ and many other Open Source tools, self-developed tools
- Daily Operation Tasks(Tickets and OnCall) according to ITIL procedure and eBay policy
- Enhance current tool and develop new tool according to new requirement from Operation Team(NOC)
Achievements:
- GMS Daily System Administrating(including 24x7 OnCall shift) — GMS is a special environment used by eBay for many special Web Products which are NOT fit for eBay Main Site, based on vSphere and other technology and we privided HOSTED service for hundards of different customers (Success Stories: eBay AU , eBay ShopingTool …
- Automation tools developping for GMS and eBay QA Test Environment based on Django framework and other technologies
- New technology hacking and researching including Cassandra, MongoDB, Celery, RabbitMQ and so forth
- Become a core member as Tools OnCall Team(provide 12x7 Technique Support) in half year
- Take over some major Tools support in the Tools Chain (e.x. NerveCenter, Nagios, Cricket, RRDTool, ActiveMQ)
- Provide technique support and help to the new team members
- Developping / Enhancing Some Tools with Perl script and JavaScript ( JQuery for WEB UI)
- SNMP Monitor with LogMatric NerveCenter
Michelin(China) Investment Co. Ltd (Jun 2006 - Nov 2008) as Senior System Engineer
Responsibilities:
- Support Linux/Unix/Windows System in Michelin Asia Pacific as System Administrator
- Enterprise Application Deployment & End User Support for Business of Michelin Asia Pacific
- Assets and Other IT Management tasks for IT/Infrastructure of Michelin Asia Pacific
Achievements:
- Setup and Deploying Server Configuration Management, Change Management and etc according to ITIL standard
- Project - Enhancement of DMS(Dealer Management System) with IBM P570(pSeries) and X Servers
- Project - Mirgation of ERP(JDE) Application from HP-UX to IBM P5(pSeries) and X Servers
- Researching, Deploying and Supporting SFA(Siebel CRM Solution) for Michelin Asia Pacific
- Technique Support Siebel Application and Oracle Database on AIX System more than 2 years
- Improving and maintaining Michelin Internal Monitor System by Open Source tools such as Nagios, Cacti, ntop and OpenNMS
- Improving the Linux/Unix System administrating in Michelin Asia Pacific
- Working with the Business team on DMS System deploying and support
- Constructing and Deploying new IT Rooms(Data Center) of Michelin China
- Other IT/Infrastructure support for Michelin Asia Pacific
iQue(China) Co. Ltd. - A Nintendo company (Oct 2003 - Jan 2006) as Linux System Engineer
Responsibilities:
- Internet Business System of iQue constructing and maintaining in IDC(Internet Data Center)
- Other IT maintaining and developing tasks in iQue IT Dept.
Achievements:
- Update the IDC system from separate servers to HA Cluster（LVS Solution）
- Setup a monitor system based on Open Source Tools(Cacti, RRDTool and etc)
- Support developing team on SCM like Bugzilla management and so forth both for iQue busi-ess and Game Developing Team
- Major developer and release manager of Product iQue@HOME
- Daily operation in IDC(Internet Data Center)(Software, OS, Database, Network and Hardware)
Huatek Software Engineering Co. Ltd. (Jul 2001 - Sep 2003) as Software engineer
Responsibilities:
- Software developing and maintaining in Projects of Agilent AT93000 Products(SOC Test System)
Achievements:
- Familiar with Software Developing process and Software Quality Control(Based on ISO2000)
- Familiar with Unix/Linux Developing tools and environment
- Administration and Usage of ClearCase in Developing
- Mastered in Linux System administration


Fresh Fruit Prices versus Canned Fruit Prices
Packaging
Peaches
Pears
Apples
Canned
$1
$2
$1
Fresh
$2
$3
$2


The greatest common divisor of integers x and y is the largest integer that evenly divides both x and y. Write a function gcd that returns the greatest common divisor of x and y. Also write a C++ program that calls the gcd function repetitively to create a table of greatest common divisor of paired integers from 1 to 20
result:


As far as I can remember, I've always been drawn to opiates. I've tried every drug imaginable. Nothing compares to opiates. I started with Vicodins and Percocets. My dad had become ill with cancer and there was no shortage of pain meds around the house. I would grab a couple and head out the door. The main benefit for me has always been the slamming confidence boost. The pills would just make me come out of my shell in a slick way. My speech was sharp and smart and I never heard that little voice. See I always analyzed things too much. I would think about stuff and go over my words in my head before I would speak them. Basically I am a very shy person. Not on opiates! I used perc's and Vic's for a couple years with no real problems. I even managed to get my hands on some liquid morphine a few times. I remember it making me throw up the first time I used that. The high and itching was surreal.
Eventually I bumped into a friend who had some little white pills. This was about 2 years ago. I remember the day like it was yesterday. He crushed up one of the pills and we each snorted a half. Ten minutes later I felt perfect. I loved everyone and the world was just right. I remember getting really talkative and thinking positive about everything and everyone. There was no side effect for me other than feeling a little bit of nausea. I could walk and think straight. It was just one of those things that you knew had to be too good to be true. As I've come to find out the only drawback to opiates for me is not having them! Oh by the way the pills were oxycontin 20mg.
Fast forward to the present time. I have seen a lot of things change myself included. The people that I know that use oxys are not the same people that I once knew. The scary thing is that I am starting to become them. These are friends that I grew up with. They have stolen from me, ripped me off in oxy deals, and will tell you anything to feed the habit. I often wonder if my situation is like any one else out there. I firmly believe that I am a better person when on opiates. I am harder working, more loving, get more things accomplished, and have much more confidence. Good luck telling that to a physician!
The most intense feeling in life for me was a 6 month relationship fueled by our love of oxys and each other. This girl was incredible. The intensity of the opiates made us feel as if we were in the most heated romance novel ever written. I had only known this girl for about a month but with overstock of opiates racing through our bodies, I can honestly say I have never felt closer to anyone in my life. The oxys make anyone in my life that I love or feel close to, feel about 100 times closer. It is really hard for me to explain the feeling in words. I remembered her crying when we were making love one night. I asked her why was she crying.  It must have been the opiates cause we only made it 6 months!
I have taken a multitude of drugs and this is the ideal high. The problem is that when the high goes away and the drugs aren't there. I have to face reality. That reality might be severe withdrawal. I recently had the flu. I threw up and went to the bathroom for 3 days straight. I ended up in the ER room hooked up to all kinds of IV's. I had lost close to 20 pounds. I thought it was just the flu. My friend stopped over with some oxys and I did one and didn't have the flu anymore. Thats when I figured out that it was withdrawal. I think it stemmed from the prior two weeks. I had a ton of oxys for about 3 weeks straight. I was using anywhere from 80 to 160 mg a day for about 20 days straight. That is all it took for my body to get hooked so be careful.
The people that I know that are junkies for oxys remind me of watching how crackheads are portrayed in movies. I live in a small rural community and have never had the pleasure of meeting a crackhead. The movies make them look like they would do anything to get some. The people I know are not much different.
I'm struggling with my opiate use. I have really cut back so that my body is not dependent on them anymore. I probably will never quit using, but this has been the hardest thing I've had to deal with in my entire life. They still have too many benefits for me. I have went to 5 job interviews over the past year. All three that I went to high on opiates I got the job, I only took the one that I'm at currently but I know that they had something to do with my success.
The best phrase I can say to everyone from my experiences would have to be the old saying 'Enjoy in moderation'!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


--- layout: default title: 404 ---
Sorry, we've misplaced that URL or it's pointing to something that doesn't exist. Visit the home page to see if you can find it manually. Thanks!


Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Serves 6
Rachel Allen’s award-winning version of the classic French quiche Lorraine can be served hot or cold. Here it is topped with a sprinkling of chive flowers.
For the pastry, place the flour, salt and butter in a food processor and pulse briefly. Add half of the beaten egg and continue to pulse. You might add a little more egg, but not too much as the mixture should be just moist enough to come together.
If making by hand, rub the butter into the flour until it resembles coarse breadcrumbs then, using your hands, add just enough beaten egg to bring it together.
With your hands, flatten out the ball of dough until it is about 2cm/¾in thick, then wrap in cling film or place in a plastic bag and leave in the fridge for at least 30 minutes or, if you are pushed for time, in the freezer for 10–15 minutes.
Remove the pastry from the fridge and place between two sheets of cling film (larger in size than your tart tin). Using a rolling pin, roll the pastry out to no thicker than 5mm/¼in or even thinner for small tartlet tins. If the tin is round, make sure to keep the pastry in a round shape and also large enough to line the base and sides of the tin.
Remove the cling film and place into a 18cm/7in quiche tin (with 3cm/1¼in high sides) If you wish, retain the top layer of cling film to help you shape the pastry. Press the pastry into the edges and, using your thumb, ‘cut’ the pastry along the edge of the tin for a neat finish. Remove the cling film, if used, prick over the base with a fork and chill the pastry in the fridge for 30 minutes or the freezer for 10 minutes. (Or cover it back up and keep in the freezer for a few weeks).
Preheat the oven to 180C/350F/Gas 4.
Line the pastry with foil, greaseproof or parchment paper, leaving plenty to come up the sides. Fill with baking beans or dried pulses. Place in the oven and bake for 15-20 minutes, or until the pastry feels dry.
For the filling, heat the oil in a frying pan and cook the bacon until crisp. Remove and set aside to drain on kitchen paper. Fry the onions gently in the same oil for a further 10 minutes. Meanwhile, whisk the eggs in a medium-sized bowl, add the cream, herbs, cheeses and bacon and onions. Mix well and add salt and freshly ground black pepper.
Remove the pastry from the oven and remove the paper and beans, pour the filling into the pastry base and return to the oven for 30-40 minutes, or until the centre has just set. Serve warm, garnished with chive flowers.


Please enter the ingredients you are looking for below, delimited by commas (eg: avocado, chili, pepper)


Hello, I am James Swift. No relationship to Taylor Swift, I knew it would be your first question. The only near famous thing I am linked to is when I was younger I was an extra in movies and some tv shows. This all took place in Washington, so nothing to famous. But I was in Sleepless in Seattle and Born to be Wild.
Work and Education
- Before starting Code Fellows I was working in insurance for Pacific Life Insurance Company.
- I was there for 5 year.
- Before that I worked for Verizon Wireless, in the technical service aspect.
- I was there for almost 9 years.
- For education I have a Bachelors in Business Administration.
- Some of those classes where online, not a terrible thing and prepared me for Covid-19 virtual learning while with Code Fellows.
- I cannot wait to keep learning about code and get out and find a job in the field.
Top 10 Countries Visited
- Spain
- Italy
- Germany
- Iceland
- France
- Korea
- Switzerland
- UK
- Mexico
- Canada


arXiv® is a registered trademark of Cornell University.
If you have a disability and are having trouble accessing information on this website or need materials in an alternate format, contact<EMAIL_ADDRESS>for assistance.


| DOSE: |
6 hits |
smoked |
Cannabis |
(plant material) |
First off, I would like to start off by saying that I consider myself an experienced pot smoker. I have smoked many types of pot and have been stoned on many occasions. But I never looked at marijuana as a strong hallucenogenic drug until this one night. The night started off by me going over my best friends (we'll call him M) house. The first thing he said to me as I walked in his door was 'Dude do you wanna smoke?!' I happily abliged. We left his house with 3 other of our friends (D, S, and K). We all went to our favorite smoking spot. Our spot is a clearing in the woods by our high school. It is our favorite spot because it is calm, serene, and out in 'nature.'
 We all sat down on a log as M proceeded to pack the bowl. As I waited for the pipe to work its way through everybody I looked around and really appreciated the beauty of our little smoking spot, nothing like being high and out in the wild at night. Then the pipe made it to me and I begin to deeply inhale some of the smoothest smoke I have ever tasted. The pipe was passed until most of the bag of weed was empty. When we were finished smoking I began to recognize that amazing feeling of being stoned.
We got up and played around a little, laughing and such. It was then I realized that I was still getting higher and higher. I looked at my watch and found that only 10 minutes had passed and the weed doesn't peak until around 20. I was in for a ride. While walking back to M's house I close my eyes and find that I have amazing closed eye visuals. They reminded me of hippy like peace colors morphing in and out of different shapes. Kind of like a rainbow kaliedoscope. Then I told everyone else to close their eyes and they'll love what they see. All I heard was laughter, and shouts of joy.
We finally made it to M's house I noticed that I even had opened eye visuals. It must have been going from the dark of night, and then into the light of M's house. But the second I walked in I started seeing the same 'hippy kaliedoscope' that I saw earlier. I then looked at the clock and saw that 45 minutes had passed since my first hit. We each drank a bottle of water to combat cottonmouth and then we decided to go on M's trampoline. The trampoline was the best part of my marijuana trip. We bounced around and laughed until we couldn't even breathe.
We then sat down on the trampoline and began to slowly rock up and down. K reminded us of how amazing it was to close our eyes and watch the show. Somehow, gently bouncing on the trampoline began to enhance my closed eye visuals by sort of sending me on a journey. One image morphed into another and it felt like I was watching an amazing movie. We bounced around for about 15 minutes and I decided to lay on my back and looked up at the stars. I remember looking at the stars and being so amazed at how many of them there was. I began to think and think and think. Thinking about the purpose of life, The vastness of the universe, and many other things that I don't normally think about.
We decided to go back in M's house to eat. I looked at his clock and saw that an hour had passed thinking and pondering. It has now been 2 hours since my first hit and I was still really high. We went into M's basement and we each quickly went through 4 boxes of cereal. The rest of the evening pretty much consisted of eating, playing with M's strobe light, and listening to music. They put on hard rock, which I hate, But at that moment it sounded so amazing.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


An Introduction
Hey there. I go by the name of John. This is a website for fun (not profit).
This will be a place to put some of my stuff up on the web. What kind of stuff you ask? Good question. I guess this post can be used to think of ideas for the site.
- document things I’ve learned
- reviews of things
- software development
- writing in general
- tech
- photography
- sports
- music
- style
- nice things in general


---
meta-title: Introducing the Visual Framework v1.4 and v2.0
title: Introducing the Visual Framework v1.4 and v2.0
cta: The Visual Framework 2.0 provides a new approach to reusability and compatibility. Existing services who cannot migrate can use the v1.4 update to more closely match the look of 2.0-based services.
filter-nav: false
filter-dropdowns: false
layout: static
pagetype: meta-pattern
location: /websites/framework-next
---
Last update: 2020-09-29
On this page
Loading...
{{#markdown}}
In 2015, we made the EBI Visual Framework 1.1 to help ensure brand consistency and the easy use of modern web design best practices -- such as responsive design, iterative maintenance cycles, and UX-tested patterns -- across the diverse range of EMBL-EBI websites and services.
In the coming years we refined the Framework with versions 1.2 and 1.3, and it continues to be a success by providing a tool set that gives you the EMBL-EBI look-and-feel and some UX baked and as written guidance. However the v1.x line does have shortcomings.
A common issue is the Foundation Framework as an underlying dependency. It is helpful as it supplies a robust library of elements like a page grid and buttons, however that choice makes it hard for services that want to use Bootstrap or already have other code that targets things like a `.button` class.
{{/markdown}}
{{#markdown}}
## What's new in v1.4
To aid in the transition we have created a non-breaking v1.4 update.
Not all sites and services will have the development capacity to update from 1.x to 2.0, so the v1.4 of the EBI VF will assist in the transition to the new VF 2.0 look. This release is designed as an easy change for existing sites and services while buying time or preventing the need to update 2.0 in many cases.
v1.4 creates a visual sympathy between the 1.x architecture and 2.0 architecture and look. Here is an example boilerplate between 1.3 and 1.4:
- [1.3 example](http://wwwdev.ebi.ac.uk/style-lab/websites/sample-site-v1.3/boilerplate/blank.html)
- [1.4 example](https://dev.ebi.emblstatic.net/web_guidelines/EBI-Framework/v1.4/index.html)
To test an upgrade you only need to replace the 1.3 references with 1.4:
- [dev.ebi.emblstatic.net/web_guidelines/EBI-Framework/v1.4/css/ebi-global.css](https://dev.ebi.emblstatic.net/web_guidelines/EBI-Framework/v1.4/css/ebi-global.css)
- [dev.ebi.emblstatic.net/web_guidelines/EBI-Framework/v1.4/js/script.js](https://dev.ebi.emblstatic.net/web_guidelines/EBI-Framework/v1.4/js/script.js)
- [dev.ebi.emblstatic.net/web_guidelines/EBI-Framework/v1.4/js/foundationExtendEBI.js](https://dev.ebi.emblstatic.net/web_guidelines/EBI-Framework/v1.4/js/foundationExtendEBI.js)
- note: as of this writing (2020-09-29) we have not yet release 1.4 on npm or the production cdn
If you have a service running 1.2 or 1.3, try changing the CSS and JS to the aboves URLs and report any concern to [khawkins<EMAIL_ADDRESS>or [on GitHub](https://github.com/ebiwd/EBI-Framework/issues/162).
### Related v1.4 links
- [Boilerplates]({{root}}websites/sample-site/)
- [GitHub issue on v1.4 planning](https://github.com/ebiwd/EBI-Framework/issues/162)
## What's next
As of September 2020, both v1.4 and v2.0 are under active development and we'll be hosting a series of consultative workshops to incorporate ideas and concerns. To register your interest email<EMAIL_ADDRESS>{{/markdown}}
{{#markdown}}
## What's new in v2.0
For services and sites that are new or are going through major redevelopments, you should use [the Visual Framework v2.0](https://visual-framework.github.io/vf-core/).
This is the version that will be actively maintained and cary the new EMBL and EMBL-EBI look and feel, it also has an improved approach to the diverse world of JS and CSS that scientific services use.
Highlights:
- a "[CSS Modules](https://github.com/css-modules/css-modules)"-style: CSS classes are prefixed (`.vf-button`). This ensures you won't get accidental spillover of classes from the Visual Framework.
- fully modular: each [button](https://www.npmjs.com/package/@visual-framework/vf-button), [grid](https://www.npmjs.com/package/@visual-framework/vf-grid), [tab](https://www.npmjs.com/package/@visual-framework/vf-tabs) or other component is a [self-contained npm-project](https://www.npmjs.com/search?q=%40visual-framework). This means you can use the prebuilt CSS and JS, or build your own mix of components to avoid "that one thing that doesn't work for you".
- easy integration of "corporate" information: if you want to show EMBL/EMBL-EBI jobs, persons, news, publications, the VF 2.0 allows [HTML-import style embeds](https://visual-framework.github.io/vf-core//components/embl-content-hub-loader/index.html?q=contentHub).
- updated UX approaches: the VF 2.0 uses a container-based approach to reduce cognitive load for users and improve engagement and task completion.
- EBI and beyond: a frequent problem of 1.x was how it was closely tied to the EMBL-EBI brand, making it of less use for services that wanted the UX lessons but not the EMBL-EBI brand. You can now bring you own colours and spacing through [design tokens](https://visual-framework.github.io/vf-core//components/vf-design-tokens/).
### Related v2.0 links
- [Visual Framework 2.0 welcome and documentation](https://visual-framework.github.io/vf-core/)
- The [COVID-19 Data Portal](https://www.covid19dataportal.org/) uses v2.0
- [Building your first page with the Visual Framework 2.0](https://www.allaboutken.com/posts/20191118-first-page-with-the-visual-framework.html)
- [Example boilerplate](https://visual-framework.github.io/vf-core/patterns/scientific-service/)
{{/markdown}}


My research focuses on understanding how galaxies and gas trace the distribution of matter at different cosmic epochs. For this purpose, I construct and analyse theoretical galaxies in a cosmological context using super computers. I compare these tracers with observations from different surveys including large cosmological ones, such as DESI. Galaxies are cosmic lanterns that show small patches of cosmic history, informing us of the changes in the space-time fabric of the Universe. A theoretical understanding of the formation of galaxies makes them useful to understand the composition and fate of the Universe.
Galaxies are hardly visible to the naked eye but telescopes are able to observed them up to very early cosmic times. In the current most successful cosmological theory, the Universe is filled with a mysterious energy form, the dark energy, and an elusive form of matter that has not yet been detected, the dark matter. The composition and fate of the Universe are the most fundamental aspects to be understood by humankind.
At Liverpool John Moores University, I started working with the BAHAMAS suite of hydrodynamical simulations. At Durham University, I started using GALFORM, a model of galaxy formation and evolution to help interpret observations of galaxies and to improve our understanding of the physics of galaxy formation.
Below you can read a summary of my first author papers.
Emission line galaxies at z~1
Press release.
PAPER 2: Do model emission line galaxies live in filaments at z~1?.
Seminar at Zaragoza summarising my work on this topic..
Star-forming emission line galaxies (ELGs) are being used as tracers of the dark matter distribution at z~1. Model [OII] emitters have luminosity functions in reasonable agreement with observations. ~95% of model ELGs are centrals hosted by haloes with M>1010.5Msunh-1. The mean halo occupation distributions of central ELGs are far away from a canonical step function and we have proposed a split contribution from disks, asymetric Gaussian, and spheroid central ELGs, soft step function. Half of the ELGs live in filaments and a third in sheets. This distribution is similar to that of star-forming galaxies selected in different ways. ELGs, star formatio rate and [OII] luminosity selected samples with equal number density, have similar large scale bias but their clustering below separations of 1h−1Mpc is different.
A new model of galaxy formation
Galaxies are thought to form within haloes of dark matter, whose gravity allows the galaxies to exist. The formation and evolution of galaxies is affected by a multitude of other processes besides gravity and computational modelling is the only way we can attempt to understand all these processes. In this work we presenta a new development of the GALFORM semi-analytical model of galaxy formation and evolution, which exploits a Millennium Simulation-class N-body run performed with the Wilkinson Microwave Anisotropy Probe 7 cosmology. We use this new model to study the impact of the choice of stellar population synthesis (SPS) model on the predicted evolution of the galaxy luminosity function.
Very high redshift galaxies
The study of high redshift galaxies, z>2.5, is fundamental in unveiling initial phases of galaxy evolution. However, the interpretation of the observational data is dramatically affected by dust attenuation. We explored the impact of dust on UV luminosities using a published model of galaxy formation. I found that UV colours are very sensitive to the shape of the extinction curve used as an input for the radiative transfer model that calculates dust attenuation, stressing the difficulty of using the UV continuum slope as a tracer of dust attenuation without an understanding of the dust characteristics and geometrical distribution in high redshift galaxies.
Massive red galaxies at z>1
The observed abundance of massive, high redshift galaxies with very red colours posed a major challenge to hierarchical clustering cosmologies. I presented the first example of a model which could explain these observations, whereas previous work had failed dramatically, by an order of magnitude. The key to understanding these objects proved to be the inclusion of heating of gas following the accretion of matter onto super-massive black holes at the centres of galaxies.
- Tabulated compilation of data from the literature: EROs number counts.
Following the breakthrough above, I studied the clustering of Extremely Red Objects (EROs) as a further test of the model. The model predicts a very different distribution of galaxies across halos of different masses than is typically assumed, which has a major influence on the clustering predictions. These predictions agree with the currently available observations, once the small size of the samples is taken into account.
Internal colour gradients
The internal properties of galaxies contain information of their recent evolution. Our analysis relates, on average, steep colour gradients to a higher presence of young stars within a galaxy. Close pairs of interacting galaxies were found to present bluer cores than the average population. We also found that nuclear activity is a marginal driver for creating steep colour gradients in massive galaxies.


André Lopes
I am a Software Engineering student and I am doing my best to become an excellent Software Engineer.
{% for project in projects %}
{% endfor %}
{% load static %}
I am a Software Engineering student and I am doing my best to become an excellent Software Engineer.


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Hello, Webauthn
SiteName:
Username:
DisplayName:
navigator.credentials.create
Challenge:
navigator.credentials.get


You will work in an online writing group to give and receive constructive feedback on the writing projects in the course. You will share drafts of your project, give one another support and feedback, and help one another keep on track in the course.
We’ve all had the experience of receiving feedback that didn’t really help us improve our work. The information may have been too vague, or it may have focused on the wrong things. Worse, the readers may have been afraid to hurt our feelings, so they didn’t tell us much of anything at all.
Our goal is to make sure feedback in this course is always helpful by focusing on formative feedback (rather than summative feedback). Let’s begin with some definitions:
Summative Feedback:
Formative Feedback:
One widely-used analogy explains the difference between these two kinds of feedback this way: A chef is using formative assessment when she tastes a dish while cooking to decide if she needs to add anything more. Diners are using summative assessment when they taste the finished dishes and share their comments.
In your writing group, you are helping one another form and improve the project. You won’t make judgments of quality; you will focus on comments that help the others in the group strengthen their projects by giving detailed and constructive feedback.
The video below, No One Writes Alone: Peer Review in the Classroom, A Guide For Students (6m 33s), demonstrates how students collaborate and provide feedback on drafts for one another. Watch the video, paying attention to the kind of feedback people give and how the authors respond to the feedback.
The online discussions that you have about your writing group should be much like the conversations that take place in the video. When you comment on someone else’s projects, you provide concrete details about what you see and what the writer can do to improve. When someone comments on your projects, remember to be open to suggestions and avoid becoming defensive.
This chapter from one of our online textbooks offers advice on teamwork and collaboration:
You will share whatever you are working on with your group in Canvas, using the Discussions tool, primarily in one of two ways:
In addition to sharing your draft, you should include some details on what you are working on and what you are interested in improving.
After you post info about your work, you will comment on the ideas of others in your group. You can respond to your group members with any of the following:
Icons all from The Noun Project and used under a CC-BY license: team by Creative Stall


001/* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017package org.apache.commons.text.similarity; 018 019/** 020 * Interface for the concept of a string similarity score. 021 * 022 *
 023 * A string similarity score is intended to have some of the properties of a metric, yet 024 * allowing for exceptions, namely the Jaro-Winkler similarity score. 025 *
 026 *
 027 * We Define a SimilarityScore to be a function d: [X * X] -> [0, INFINITY) with the 028 * following properties: 029 *
 030 *
 031 *
d(x,y) >= 0, non-negativity or separation axiom
 032 *
d(x,y) == d(y,x), symmetry.
 033 *
 034 * 035 *
 036 * Notice, these are two of the properties that contribute to d being a metric. 037 *
 038 * 039 * 040 *
 041 * Further, this intended to be BiFunction<CharSequence, CharSequence, R>. 042 * The apply method 043 * accepts a pair of {@link CharSequence} parameters 044 * and returns an R type similarity score. We have omitted the explicit 045 * statement of extending BiFunction due to it only being implemented in Java 1.8, and we 046 * wish to maintain Java 1.7 compatibility. 047 *
 048 * 049 * @param <R> The type of similarity score unit used by this EditDistance. 050 * @since 1.0 051 */ 052public interface SimilarityScore<R> { 053 054 /** 055 * Compares two CharSequences. 056 * 057 * @param left the first CharSequence 058 * @param right the second CharSequence 059 * @return the similarity score between two CharSequences 060 */ 061 R apply(CharSequence left, CharSequence right); 062 063}